Machine Learning Comprehensive Introduction (Giới thiệu về Học máy) - Chittaranjan Hota

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Artificial Intelligence (CS F407) Machine Learning: Introduction Chittaranjan Hota Professor, Computer Science & Information Systems Department BITS Pilani Hyderabad Campus, Hyderabad hota@hyderabad.bits-pilani.ac.in

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Some tasks… Content Promotion, Content Price Modeling, Adaptive Streaming QoE… ‘cocktail party problem’

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What is Learning? • “Learning denotes changes in a system that enables a system to do the same task more efficiently the next time.” – Herbert Simon (Writing your AI Midsem and then Compre…) • Learning is the process of acquiring new or modifying existing knowledge, behaviors, skills, values, or preferences- Wiki • Humans learn from experiences….

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What is Machine Learning? x y x1 1 2 … … y System 2 x h h, ,...,h y Input VariablesN 1 2 K M Output Variables Hidden Variables: data data program program output output

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Defining the Learning Task Improve on task, T, with respect to performance metric, P, based on experience, E (Tom Mitchell, 1997). T: Playing chess P: Percentage of games won against an arbitrary opponent E: Playing practice games against whom? T: Recognizing hand-written words P: Percentage of words correctly classified E: Database of human-labeled images of handwritten words T: Categorize email messages as spam or legitimate. P: Percentage of email messages correctly classified. E: Database of emails, some with human-given labels

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Why Machine Learning is important? • Machine learning is programming computers to optimize a performance criterion using example data. • There is no need to “learn” to calculate payroll • Learning is used when: – Human expertise does not exist • x: Bond graph for a new molecule, f(x): Predicted binding strength to AIDS protease molecule. • Navigating on Mars – Humans are unable to explain their expertise • x: Bitmap picture of hand-written character, f(x): Ascii code of the character • Speech recognition

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Continued… – Solution changes in time • x: Description of stock prices and trades for last 20 days, f(x): Recommended stock transactions • Routing on a computer network – Solution needs to be adapted to particular cases • x: Incoming email message, f(x): Importance score for presenting to user (or deleting without presenting) • User biometrics – Relationship and correlations can be hidden within large amounts of data – New knowledge is being constantly discovered by humans

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Applications of Machine Learning • natural language processing • search engines • medical diagnosis • detecting credit card fraud • stock market analysis classifying DNA sequences Apache Singa • • speech and handwriting recog. • Spam filtering • Financial Investments (Real estate or Postal deposits) • game playing • adaptive websites • robot locomotion • structural health monitoring • sentiment analysis (Frameworks/Tools)

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Few more examples and algos… Rs. 5 lakhs ? lakhs Rs. 80 lakhs Example of Linear Regression… Gradient descent

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Few more examples and algos… Boss Cheap Bulk Identifying spam mails Dollar Missing title etc… Naïve Bayes

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Few more examples and algos… Recommending Apps to different users … Gender Age App F 17 Instagram F 28 WhatsApp Decision Tree. M 35 FB messenger F 44 WhatsApp M 13 Instagram M 16 Instagram

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Issues in Machine Learning • What algorithms are available for learning a concept? How well do they perform? • How much training data is sufficient to learn a concept with high confidence? • Are some training examples more useful than others? • What are the best tasks for a system to learn? • What is the best way for a system to represent it’s knowledge?

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ANNs: Nerve cell or neuron Nervous Computational System Abstraction Neuron Node Dendrites Input link and propagation Cell Body Combination function, threshold, activation function Axon Output link Synaptic Connection strength strength/weight Local, • A healthy human brain has around parallel computat 100 billion neurons ion • A neuron may connect to as many as 100,000 other neurons Acknowledgement: Few slides are adopted from R J Mooney’s class slides

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